Some Basics of Network Analysis
Network analysis is all about connections (surprise). What sets network analytic approaches apart form ‘standard’ quantitative research is really the inherent inter-dependence of the data. Meaning, observations only exist BECAUSE they are connected. For example, multi-national trade relations require at least two countries to trade goods. Otherwise we can not observe the phenomenon.
In network terms, each country in the trade example would be called a node (or vertex, pl. vertices) and the trade through which the are connected would be an edge (or tie). Nodes and edges can have additional features, as we will see below.
In network analysis, researchers can be interested in the position of specific nodes or in the overall network structure. There are several quantitative measures you can calculate for this, some of which are introduced below. The network measures can then be both explanatory or dependent variables, depending on your research question.
Network Analysis in R
There are several packages for network analysis in R:
- igraph (most popular, syntax rather cryptic, incl. plotting functions)
- network (largely overlaps with igraph, syntax is more straight-forward, part of the ‘statnet family’)
- tidygraph (builds on igraph and includes most of its functionality, tidy syntax, no plotting functions)
- ggraph (for plotting, syntax like ggplot, mostly used together with tidygraph)
- visNetwork, threejs, networkD3, ndtv-d3 (interactive plotting, order ascending in complexity)
This tutorial will only feature tidygraph, ggraph, and visNetwork. See the Resources section for more great material, also for the other packages.
Contents of Networks & Basic Graphs
That networks consist of two types of data (nodes and edges) is also visible in network objects in R. In tidygraph, these objects are named tbl_graph. Let’s construct one of those with some example node and edge data.
node_list <- tibble(id = c(1:5))
edge_list <- tibble(from = c(1,1,1,2,3,3,3,4,5,5,5), to = c(2,2,3,4,2,4,5,5,2,2,2)) %>%
group_by(from, to) %>%
summarise(weight = n())
undir_net <- tbl_graph(nodes = node_list, edges = edge_list, directed = FALSE, node_key = "id")
undir_net## # A tbl_graph: 5 nodes and 8 edges
## #
## # An undirected simple graph with 1 component
## #
## # Node Data: 5 x 1 (active)
## id
## <int>
## 1 1
## 2 2
## 3 3
## 4 4
## 5 5
## #
## # Edge Data: 8 x 3
## from to weight
## <int> <int> <int>
## 1 1 2 2
## 2 1 3 1
## 3 2 4 1
## # ... with 5 more rowsThe minimum content of node data is an identifier variable (here id). The identifier has to appear in the edge data as well. There it indicates which nodes are connected to one another (here from and to). I already added a weight to the edges. This is simply a count of how many connections are between a pair of nodes. The resulting graph is undirected (directed = FALSE), which can be translated as a ‘mutual relationship’.
We can now plot this object with ggraph, which basically works like ggplot2 with some network specific features. The layout option defines the algorithm that is responsible for how the nodes and edges are positioned to one another. The layouts available are the same as in the igraph function layout_with_*. ‘kk’ stands for Kamada-Kawai, a very common layout algorithm that distributes nodes equally in space.
pal <- Manu::get_pal("Kereru") # define color palette for plotting (https://github.com/G-Thomson/Manu)
# undirected, unweighted, no attributes
ggraph(undir_net, layout = "kk") +
geom_edge_link() +
geom_node_point(size = 10, color = pal[1]) +
geom_node_text(aes(label = id),
colour = "white", vjust = 0.4) +
theme_graph() # theme without axes, gridlines etc.
Undirected, unweighted graph without attributes
By setting directed = TRUE the edges in the graph are now directed towards some nodes. Some real-world examples are cash or trade flows, social media communication, or non-mutual friendship 😢
dir_net <- tbl_graph(nodes = node_list, edges = edge_list, directed = TRUE, node_key = "id")
ggraph(dir_net, layout = "kk") +
geom_edge_link(arrow = arrow(angle = 15, type = "closed", length = unit(4, "mm")),
end_cap = circle(4, "mm")) + # so arrows don't overlap nodes
geom_node_point(size = 10, colour = pal[1]) +
geom_node_text(aes(label = id),
colour = "white", vjust = 0.4) +
theme_graph()
Directed, unweighted graph without attributes
We can also visualise the weight of the edges to show how ‘strong’ certain ties are, using the width argument.
# undirected, weighted, no attributes
ggraph(undir_net, layout = "kk") +
geom_edge_link(aes(width = weight),
alpha = 0.7) +
geom_node_point(size = 10, colour = pal[1]) +
geom_node_text(aes(label = id),
colour = "white", vjust = 0.4) +
theme_graph(base_family="sans") # provide font family, otherwise can't render document (Windows)
Undirected, weighted graph without attributes
We are often interested in certain attributes of nodes or edges. To add some to the example data, tidygraph needs to know which data we want to alter (nodes or edges). Therefore, the package contains the activate function. Then, we can manipulate data with all the dplyr verbs we know and (mostly) love.
# undirected, unweighted, with attributes
undir_net.att <- undir_net %>%
activate(nodes) %>%
mutate(Preference = rep(c("Python", "R"), c(3, 2))) %>%
activate(edges) %>%
mutate(Relationship = sample(c("Friends", "Foes"), 8, replace = TRUE))
ggraph(undir_net.att, layout = "kk") +
geom_edge_link(aes(label = Relationship),
angle_calc = "along", label_dodge = unit(2.5, "mm"), label_push = unit(10, "mm"),
alpha = 0.7) +
geom_node_point(aes(colour = Preference),
size = 10) +
scale_color_manual(values = pal[c(1,2)]) +
geom_node_text(aes(label = id),
colour = "white", vjust = 0.4) +
theme_graph(base_family="sans") # provide font family, otherwise can't render document (Windows)
Undirected, unweighted graph with attributes
The concept of attributes can be pushed further: we can say that nodes are of different types. This results in bipartite (or two-mode) networks, where nodes of the same type are not directly connected to one another, but only through the nodes of the other type. This could be employees in firms or authors of research papers, for example.
bipart_net <- play_bipartite(8, 2, p=0.8, directed = FALSE) %>% # play_* generates different types of networks
activate(nodes) %>%
mutate("Node.type" = as.character(if_else(type==TRUE, "Firm", "Employee")))
ggraph(bipart_net, layout = "stress") +
geom_edge_link() +
geom_node_point(aes(shape = Node.type , color = Node.type),
size = 6) +
scale_color_manual(values = pal[c(1,2)]) +
theme_graph(base_family="sans") # provide font family, otherwise can't render document (Windows)
Bi-partite graph
Network Data in the Wild: The Matrix Trilogy
As described above, we need a node and an edge set to do network analysis in R. However, life out there seldom provides us with data in this specific format. This is contrary to, e.g., survey data that is already in a ready-to-use format (apart from some variable recoding etc.).
Instead, networks are mostly displayed as different kinds of matrices. How these relate to one another can be confusing and is something we don’t usually have to deal with in standard quant research. There are three types of matrices that can be used to describe a network and ‘translating’ them into the desired format varies by type. Often your data isn’t even a matrix yet. In that case, you first have to figure out to which of the following formats you can/should transform it.
Adjacency Matrix (aka. Sociomatrix)
An adjacency matrix is basically a cross-table of the same elements (mostly of the nodes), and is therefore square. The cell values can be restricted to 0/1 to indicate whether there are any connections between the elements, or be a count of the connections.
adj_mat <- matrix(sample(0:3, 16, replace = TRUE), nrow = 4)
colnames(adj_mat) <- rownames(adj_mat) <- LETTERS[1:4]
adj_mat## A B C D
## A 1 1 3 0
## B 0 3 2 1
## C 3 1 2 1
## D 2 3 1 1tidygraph can create tbl_graph objects from a variety of data formats, also adjacency matrices.
## # A tbl_graph: 4 nodes and 10 edges
## #
## # An undirected multigraph with 1 component
## #
## # Node Data: 4 x 1 (active)
## name
## <chr>
## 1 A
## 2 B
## 3 C
## 4 D
## #
## # Edge Data: 10 x 3
## from to weight
## <int> <int> <dbl>
## 1 1 1 1
## 2 1 2 1
## 3 1 3 3
## # ... with 7 more rowsIncidence Matrix
An incidence matrix in contrast, is a cross-table of different elements, e.g. nodes and edges or different types of nodes as in a bipartite graph.
inc_mat <- matrix(sample(0:3, 12, replace = TRUE), nrow = 3)
rownames(inc_mat) <- LETTERS[1:3]
colnames(inc_mat) <- letters[1:4]
inc_mat## a b c d
## A 1 1 0 0
## B 3 3 3 3
## C 2 3 2 3Converting the incidence matrix to a network results in this:
## # A tbl_graph: 7 nodes and 10 edges
## #
## # A bipartite simple graph with 1 component
## #
## # Node Data: 7 x 2 (active)
## type name
## <lgl> <chr>
## 1 FALSE A
## 2 FALSE B
## 3 FALSE C
## 4 TRUE a
## 5 TRUE b
## 6 TRUE c
## # ... with 1 more row
## #
## # Edge Data: 10 x 3
## from to weight
## <int> <int> <dbl>
## 1 1 4 1
## 2 1 5 1
## 3 2 4 3
## # ... with 7 more rowsThe type variable is a logical that denotes the node-type in a bipartite network.
Edgelist
We already know this format and it’s just a matrix in disguise. It consists of two columns with labels/names of elements that are connected to one another, and sometimes a weight column. It’s actually just the edgelist as in the example network before, yay!
## # A tibble: 8 x 3
## # Groups: from [5]
## from to weight
## <dbl> <dbl> <int>
## 1 1 2 2
## 2 1 3 1
## 3 2 4 1
## 4 3 2 1
## 5 3 4 1
## 6 3 5 1
## 7 4 5 1
## 8 5 2 3## # A tbl_graph: 5 nodes and 8 edges
## #
## # An undirected simple graph with 1 component
## #
## # Node Data: 5 x 1 (active)
## name
## <chr>
## 1 1
## 2 2
## 3 3
## 4 4
## 5 5
## #
## # Edge Data: 8 x 3
## from to weight
## <int> <int> <int>
## 1 1 2 2
## 2 1 3 1
## 3 2 4 1
## # ... with 5 more rowsAn edgelist is sufficient to create a network, but often we have some additional data with node attributes like the R and Python users in the example.
Real-life Application: Co-authorship Networks
For a term paper, I downloaded publication lists of the SOCIUM research centre. After bringing them into a reasonable format - that is an incidence matrix where authors are connected to publications (bipartite network) - I turned everything into an adjacency matrix connecting authors to one another (this is the only matrix algebra I know). I then added some author attributes like department membership, department position, and gender.
soc_inc <- readRDS(here("data", "socpub_bipart.RDS")) # incidence matrix with authors as rows, publications as cols
rownames(soc_inc)[1:3]## [1] "Aagaard, Lise" "Abholz, Heinz-Harald" "Abramowski, Ruth"## [1] "Social Stratification and Social Movements.; 2020"
## [2] "Routledge Handbook of; 2020"
## [3] "Die militaerische Elite des Kaiserreichs. 24 Lebenslaeufe, Darmstadt:; 2020"## Social Stratification and Social Movements.; 2020
## Aagaard, Lise 0
## Abholz, Heinz-Harald 0
## Abramowski, Ruth 0
## Routledge Handbook of; 2020
## Aagaard, Lise 0
## Abholz, Heinz-Harald 0
## Abramowski, Ruth 0
## Die militaerische Elite des Kaiserreichs. 24 Lebenslaeufe, Darmstadt:; 2020
## Aagaard, Lise 0
## Abholz, Heinz-Harald 0
## Abramowski, Ruth 0soc_adj <- soc_inc %*% t(soc_inc) # inc. matrix x transponse(inc. matrix) = adj. matrix
rownames(soc_adj)[1:3]## [1] "Aagaard, Lise" "Abholz, Heinz-Harald" "Abramowski, Ruth"## [1] "Aagaard, Lise" "Abholz, Heinz-Harald" "Abramowski, Ruth"## Aagaard, Lise Abholz, Heinz-Harald Abramowski, Ruth
## Aagaard, Lise 3 0 0
## Abholz, Heinz-Harald 0 1 0
## Abramowski, Ruth 0 0 4## # A tbl_graph: 1239 nodes and 5635 edges
## #
## # An undirected multigraph with 45 components
## #
## # Node Data: 1,239 x 1 (active)
## name
## <chr>
## 1 Aagaard, Lise
## 2 Abholz, Heinz-Harald
## 3 Abramowski, Ruth
## 4 Acksel, Britta
## 5 Adam, Christian
## 6 Agartan, Tuba I.
## # ... with 1,233 more rows
## #
## # Edge Data: 5,635 x 3
## from to weight
## <int> <int> <dbl>
## 1 1 1 3
## 2 1 28 3
## 3 1 141 3
## # ... with 5,632 more rows#soc_2mode <- as_tbl_graph(socpub_inc, directed = FALSE) # this would give the bipartite/two-mode network
#soc_2mode
# read in author attributes and add to graph
auth_att <- as.data.frame(read_excel(here("data", "auth_attributes.xlsx")))
auth_att %<>%
mutate(socium = if_else(is.na(dep_cat), 0, 1)) %>% # indicator for socium member
mutate(dep_cat = case_when(dep_cat == 1 ~ "Theory", # label departments
dep_cat == 2 ~ "Polit. Economy",
dep_cat == 3 ~ "Ineq. in Welfare Societies",
dep_cat == 4 ~ "Life Course",
dep_cat == 5 ~ "Health")) %>%
mutate(al = if_else(socium==1 & is.na(al), 0, al),
agl = if_else(socium==1 & is.na(agl), 0, agl)) %>%
rename("Head Department" = "al", "Head Working Grp." = "agl")
soc_attr <- soc_1mode %>%
activate(nodes) %>%
left_join(auth_att)
soc_attr %N>% # shortcut for activate(nodes), %E>% for edges
as_tibble## # A tibble: 1,239 x 14
## name dep1 dep2 dep3 dep4 dep5 dep6 dep_cat dep_cat2 female
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 Aaga~ NA NA NA NA NA NA <NA> NA NA
## 2 Abho~ NA NA NA NA NA NA <NA> NA NA
## 3 Abra~ NA NA 1 NA NA NA Ineq. ~ NA 1
## 4 Acks~ NA NA NA NA NA NA <NA> NA NA
## 5 Adam~ NA NA NA NA NA NA <NA> NA NA
## 6 Agar~ NA NA NA NA NA NA <NA> NA NA
## 7 Ahau~ NA NA NA NA NA NA <NA> NA NA
## 8 Albe~ NA NA NA NA NA NA <NA> NA NA
## 9 Alje~ 1 NA NA NA NA NA Theory NA 0
## 10 Alsc~ NA NA NA NA NA NA <NA> NA NA
## # ... with 1,229 more rows, and 4 more variables: `Head Department` <dbl>,
## # `Head Working Grp.` <dbl>, tot_pub <dbl>, socium <dbl>Centrality Measures
Centrality measures can be calculated to identify the “importance” of an actor within a network. As importance can be defined in various ways, there are also different types of centrality (31 of which are built into tidygraph). I will introduce only the four most widely used measures here: degree, betweenness, eigenvector, and closeness centrality.
Degree Centrality
Degree centrality simply counts the edges attached to a node. In directed networks, you can also specify whether you want to look at the in- or out-degree (edges going to or from a node). In the co-authorship network, the most central actor is the one with the most co-authors (multiple ties are allowed).
# add cent. measures to nodes
soc_meas <-
soc_attr %N>%
mutate(degree = centrality_degree(),
betweenness = round(centrality_betweenness(), 2),
eigen = round(centrality_eigen(), 2),
closeness = centrality_closeness())
# show name and value of top 10 nodes
soc_meas %N>%
arrange(desc(degree)) %>%
select(name, degree) %>%
as_tibble() %>%
head(10)## # A tibble: 10 x 2
## name degree
## <chr> <dbl>
## 1 Glaeske, Gerd 165
## 2 Rothgang, Heinz 147
## 3 Haunss, Sebastian 84
## 4 Hoffmann, Falk 75
## 5 Mozygemba, Kati 60
## 6 Giersiepen, Klaus 52
## 7 Sommer, Moritz 50
## 8 Gerhardus, Ansgar 48
## 9 Nullmeier, Frank 48
## 10 Czwikla, Jonas 41Betweenness Centrality
Betweenness centrality counts the shortest paths that go through a node. A path is any series of connected (adjacent) nodes, and it is shorter the fewer nodes have to be passed through. If many shortest paths go through a node, it is an important actor for the transmission efficiency of the network. A node with high betweenness centrality can also be interpreted as a bridging actor, working like a hub through which other nodes are connected.
## # A tibble: 10 x 2
## name betweenness
## <chr> <dbl>
## 1 Rothgang, Heinz 238506.
## 2 Glaeske, Gerd 155643.
## 3 Nullmeier, Frank 135213.
## 4 Haunss, Sebastian 91706.
## 5 Schneider, Steffen 83009.
## 6 Huinink, Johannes 77915.
## 7 Schimank, Uwe 68006.
## 8 Leibfried, Stephan 65570.
## 9 Buhr, Petra 63849.
## 10 Braun, Bernard 57192.Closeness Centrality
Closeness Centrality also includes the shortest paths. It is the inverse of the average distance (defined as shortest path) between a node and all other nodes. It basically displays how close a node is to all other nodes.
## # A tibble: 10 x 2
## name closeness
## <chr> <dbl>
## 1 Rothgang, Heinz 0.00000502
## 2 Glaeske, Gerd 0.00000502
## 3 Mueller, Rolf 0.00000501
## 4 Nullmeier, Frank 0.00000501
## 5 Schneider, Steffen 0.00000501
## 6 Braun, Bernard 0.00000501
## 7 Giersiepen, Klaus 0.00000501
## 8 Schmid, Achim 0.00000501
## 9 Schmiemann, Guido 0.00000501
## 10 Hoffmann, Falk 0.00000501If a network is largely disconnected (it is split into multiple components or even isolates), it doesn’t really make sense to calculate closeness centrality because some paths are simply missing. Therefore, I re-define it only for the largest component:
larg_comp <-
soc_meas %N>%
mutate(component = group_components() %>%
factor()) %>%
group_by(component) %>%
mutate(comp_size = n()) %>%
ungroup() %>%
filter(comp_size == max(comp_size)) %>%
mutate(closeness = centrality_closeness())
larg_comp %N>%
arrange(desc(closeness)) %>%
select(name, closeness) %>%
as_tibble() %>%
head(10)## # A tibble: 10 x 2
## name closeness
## <chr> <dbl>
## 1 Rothgang, Heinz 0.000299
## 2 Glaeske, Gerd 0.000275
## 3 Mueller, Rolf 0.000273
## 4 Nullmeier, Frank 0.000267
## 5 Schneider, Steffen 0.000266
## 6 Braun, Bernard 0.000265
## 7 Giersiepen, Klaus 0.000258
## 8 Schmid, Achim 0.000252
## 9 Schmiemann, Guido 0.000252
## 10 Hoffmann, Falk 0.000251We can see that even the largest values are still pretty small.
Eigenvector Centrality
Eigenvector centrality not only looks at how many ties a node (ego) has, but also how many ties its alters have. High eigenvector centrality means being connected to many other well connected nodes.
## # A tibble: 10 x 2
## name eigen
## <chr> <dbl>
## 1 Mozygemba, Kati 1
## 2 Gerhardus, Ansgar 0.96
## 3 Van der Wilt, Gert Jan 0.93
## 4 Brereton, Louise 0.91
## 5 Refolo, Pietro 0.91
## 6 Sacchini, Dario 0.91
## 7 Tummers, Marcia 0.91
## 8 Wahlster, Philip 0.91
## 9 Rehfuess, Eva Annette 0.9
## 10 Lysdahl, Kristin Bakke 0.89Static Plotting with ggraph
We can now plot the network highlighting the different centrality measures. To increase visibility, the network is reduced to SOCIUM-members.
soc_sub <-
soc_meas %>%
filter(socium == 1)
larg_comp_sub <-
larg_comp %>%
filter(socium == 1)
ggraph(soc_sub, layout = "fr") +
geom_edge_link() +
geom_node_point(aes(fill = as.factor(dep_cat)),
shape = 21, size = 2) +
labs(fill = "Department", caption = "Co-authorship network of SOCIUM members.") +
scale_fill_manual(values = pal)+
theme_graph(base_family="sans")
ggraph(soc_sub, layout = "fr") +
geom_edge_link(alpha = .7) +
geom_node_point(aes(fill = as.factor(dep_cat), size = degree),
shape = 21) +
geom_node_label(aes(filter = degree>quantile(degree, p=.95), label = name),
vjust = 3, size = 2, alpha = 0.8, label.padding = 0.1, repel = TRUE) +
labs(fill = "Department", size = "Degree Centrality",
caption = "Co-authorship network of SOCIUM members.\nLabelled highest 5% in degree.") +
scale_fill_manual(values = pal)+
theme_graph(base_family="sans")
ggraph(soc_sub, layout = "fr") +
geom_edge_link(alpha = .7) +
geom_node_point(aes(fill = as.factor(dep_cat), size = betweenness),
shape = 21) +
geom_node_label(aes(filter = betweenness>quantile(betweenness, p=.95), label = name),
vjust = 3, size = 2, alpha = 0.8, label.padding = 0.1, repel = TRUE) +
labs(fill = "Department", size = "Betweenness Centrality",
caption = "Co-authorship network of SOCIUM members.\nLabelled highest 5% in betweenness.") +
scale_fill_manual(values = pal)+
theme_graph(base_family="sans")
ggraph(larg_comp_sub, layout = "fr") + # plot only largest component for closeness
geom_edge_link() +
geom_node_point(aes(fill = as.factor(dep_cat), size = closeness),
shape = 21) +
geom_node_label(aes(filter = closeness>quantile(closeness, p=.95), label = name),
vjust = 3, size = 2, alpha = 0.8, label.padding = 0.1, repel = TRUE) +
labs(fill = "Department", size = "Closeness Centrality",
caption = "Co-authorship network of SOCIUM members.\nLabelled highest 5% in closeness.") +
scale_fill_manual(values = pal)+
theme_graph(base_family="sans")
ggraph(soc_sub, layout = "fr") +
geom_edge_link(alpha = .7) +
geom_node_point(aes(fill = as.factor(dep_cat), size = eigen),
shape = 21) +
geom_node_label(aes(filter = eigen>quantile(eigen, p=.99), label = name),
vjust = 3, size = 2, alpha = 0.8, label.padding = 0.1, repel = TRUE) +
labs(fill = "Department", size = "Eigenvector Centrality",
caption = "Co-authorship network of SOCIUM members.\nLabelled highest 1% in eigenvector.") +
scale_fill_manual(values = pal)+
theme_graph(base_family="sans")
Interactive Plotting with visNetwork
Interactive plotting is a great way to explore network data in more detail, especially for larger networks. The visNetwork package offers nice customization possibilities, and the result is zoom-, click-, and drag-able. Give it a try!
One downside is that it the network object has to be split into a node and edge data frame and their variables have to be given specific names, like “id”, “label”, or “group” if you want to use them for plotting. See here or here for more documentation.
node_df <- soc_sub %N>%
mutate(id = c(1:240)) %>%
rename(label = name, group = dep_cat, size = degree) %>%
arrange(as.factor(group)) %>%
as.data.frame()
edge_df <- soc_sub %E>%
filter(!edge_is_loop()) %>% # remove loops
as.data.frame()
(interact_plot <-
visNetwork(node_df, edge_df, width = "100%", height = "600px",
main = "Interactive plot of SOCIUM co-authorship network",
footer = "Nodes sized according to degree centrality.") %>%
visNodes(shadow = TRUE, font = list(size = 30)) %>%
visGroups(groupname = "Theory", color = pal[5]) %>% # have to specify groups individually
visGroups(groupname = "Polit. Economy", color = pal[4]) %>%
visGroups(groupname = "Ineq. in Welfare Societies", color = pal[2]) %>%
visGroups(groupname = "Life Course", color = pal[3]) %>%
visGroups(groupname = "Health", color = pal[1]) %>%
visIgraphLayout(layout = "layout_with_fr") %>%
visLegend(main="Department", position="right", ncol=1, zoom = FALSE) %>% # can also be manually created
visOptions(selectedBy = "group"))Resources
Great intro using different packages, good example of how to create/manipulate node and edge sets:
https://www.jessesadler.com/post/network-analysis-with-r/Intro to
tidygraphby the creator Mr. Thomas Lin Pedersen himself:
https://www.data-imaginist.com/2017/introducing-tidygraph/ (reference manual: https://tidygraph.data-imaginist.com/reference/index.html)Extensive overview of visualisation possibilities, also for interactive plots:
https://kateto.net/network-visualizationAn application of
tidygraphanggraphwith Game of Thrones data:
https://www.shirin-glander.de/2018/03/got_network/Extensive
ggraphintro:
http://mr.schochastics.net/netVizR.html